Nastel IT Operational Analytics (ITOA)
Machine learning to detect anomalies, behavior and sentiment
Accelerate decisions, satisfy customers, innovate continuously.
A Fresh Approach to Analytics
Predict Issues and Take Pro-Active Action
It’s a given that IT organizations and other lines of business want to find data outliers faster and sense problem conditions before they actually affect users. But ever-growing volumes of business data and never-ending streams of event data overwhelm traditional analytic methods. These approaches just don’t scale. A fresh approach to analytics is to leverage machine learning which improves over time and addresses novel situations.READ MORE
Although many IT organizations field basic analytic tools sufficient to keep MTTR to an acceptable level, they need more sophisticated capabilities to answer questions like:
“How does the performance of IT activities and operations impact our business?”
“Is there a way to understand these dynamic interplays in real-time to optimize intelligent day-to-day management of the business?”
To answer these business-centric questions and provide actionable guidance for decision-makers.
Nastel for ITOA fuses:
Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms.
Raw information handling and analytics speed.
END TO END
End-to-end business transaction tracking that spans technologies, tiers, and organizations.
Intuitive, easy-to-use data visualizations and dashboards.
All of these capabilities fuse seamlessly across dynamic IT environments, from mobile to mainframe, and provide the broad array of analytic and decision-support capabilities needed by developers, IT admins, and business analysts to satisfy enterprise-grade operations intelligence and APM need.
Nastel provides multiple machine learning methodologies that learn and improve their analysis over time without any dependency on writing rules.
– This algorithm is based on Robust Principal Component Analysis (RPCA)
– Detects data outliers within massive amounts of event ‘noise’ using machine-learning-based anomaly detection
– Detect anomaly in a period of time
– The function defects unusual elapsed times and learns if they become normal
– If this anomaly keeps reoccurring in this season, it becomes designated as “Not Normal”
Bayesian Conditional Probability
– Classifies behavior and learns over time
– Addresses the problem of judging documents as belonging to one category or the other
(such as spam or legitimate, sports or politics)
– Used to categorize sentiment or behavior
– Can provide predictive assessments using conditional probability
– This can be trained on either the results of a query or data manually specified by users
A method for the analysis and representation of complex networks. Power graph analysis can be thought of as a lossless compression algorithm for graphs.
The ability to estimate or conclude something by assuming that existing trends will continue or a current method will remain applicable.
Root Cause Analysis
Root cause analysis (RCA) is a systematic process for identifying “root causes” of problems or events and an approach for responding to them. RCA is based on the basic idea that effective management requires more than merely “putting out fires” for problems that develop, but finding a way to prevent them.
Get activity Compute anomaly(Avg(ElapsedTime), ‘hour/day’) where startTime > ‘2017-01-02’ and starttime < ‘2017-02-01’ Group By StartTime Bucketed by hour show as linechart
Figure 1: This query analyzes activities and detects hours in the day with unusual elapsed times
Sample Query:jKql> get number of events ‘SQL.insert.note’ where setname contains ‘Sentiment’ group by setname show as pie chart‘
Figure 2: This query reads the note the Customer Service Rep entered about the customer and uses this to analyze customer sentiment. This is used to categorize users and predict their future behavior based on their level of satisfaction.
Predictive Anomaly Detection and Machine Learning
Nastel for ITOA real-time “smart” analytics for anomaly detection makes the task of detecting problems and combining emerging trends and subtle behavior patterns into a clear picture of how IT operations are affecting business outcomes much easier and faster. IT professionals and business stakeholders can now make more informed and rapid decisions based on business insights formerly hidden in a multitude of high-volume data sources.
This major advance in anomaly detection and machine learning is based on extensive enhancements and extensions to the open source code contribution by Netflix in their Robust Anomaly Detection (RAD) project. Utilizing machine learning technology, Nastel for ITOA rapidly improves over time at predicting, sensing, and evaluating the exact nature of performance issues.
Potential use cases include almost any industry vertical where subtle data outliers demand fast reactions and decisions. Some examples include actual or potential financial system security breaches, non-compliance, supply chain issues that potentially cascade rapidly into major delivery problems affecting customers…the list is almost endless.
Developers can leverage Nastel’s® AutoPilot® ready-to-use smart analytics without the burden of constantly writing rules to make sense of data.
Business analysts are armed with advanced tools to better understand the behavior of users and immediately understand what is normal or expected behavior, and what is not.
Enterprise-grade “Fast Data” Handling & Analytics Speed
The presence of fast data — high-velocity streaming information with short-term value generated from a large multitude of IT infrastructure devices and instrumentation sources—can easily overwhelm existing analytic and APM tools.
Nastel for ITOA platform architecture
Nastel has released the next generation of its high-speed analytics platform incorporating real-time parallel data processing grids and FatPipes™ orchestration: Nastel’s for ITOA.
Designed for extreme scalability, Nastel for ITOA Lambda architecture provides the data management and processing bandwidth necessary to handle the largest enterprise use cases, regardless of data source or level of high-velocity data flows.
Nastel’s® AutoPilot® deep examination of transaction message payloads enables linkage of IT activities and behavior to expected and actual business outcomes
Following are several examples of actual Business Milestones displays drawing upon Nastel’s transaction tracking capabilities.
Example 1: Business Milestones display reflecting a SWIFT-based financial payment topology with red color bars flagging problem areas for the user
Example 2: Business Milestones for product order transactions, from initiation to shipment, showing clickable drill-down detail screen
For analysts and other non-technical business users, Nastel’s new Launchpad makes it easy to send data to Nastel AutoPilot, automatically create intuitive dashboard displays, or simply walk users through sample business use case setups that could be used as templates for their own purposes. Enterprise organizations now have an easy means of applying analytics to the data streaming from their applications to Nastel AutoPilot for ITOA.
By adding intuitive workflows plus wizards, use-case templates, and dashboard launch-pad features, the net result is an extremely fast and capable APM platform that serves the analysis and reporting needs of everyone from developers, IT admins, telecom managers, and business analysts.”
Example of AutoPilot landing page, showing access to analytics, demos, data dashboard, and explanations of sample use cases
Example of wizard-guided data analysis process for business specialists.
Example of candlestick data presentation in the context of memory utilization.
Compliance – detecting potential or actual breaches in responsibility.
FALSE POSITIVE AVOIDANCE
Preventing false problem alarms
Understanding trends across composite applications.
Determining the ability to handle rapid increases in load.